Overview

Dataset statistics

Number of variables13
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.6 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with quantity_invoices and 3 other fieldsHigh correlation
recency_days is highly correlated with quantity_invoicesHigh correlation
quantity_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
quantity_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
quantity_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with quantity_products and 1 other fieldsHigh correlation
gross_revenue is highly correlated with quantity_invoices and 1 other fieldsHigh correlation
quantity_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
quantity_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
quantity_products is highly correlated with quantity_invoicesHigh correlation
avg_ticket is highly correlated with quantity_returns and 1 other fieldsHigh correlation
quantity_returns is highly correlated with avg_ticketHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
gross_revenue is highly correlated with quantity_invoices and 2 other fieldsHigh correlation
quantity_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
quantity_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
quantity_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with quantity_itemsHigh correlation
gross_revenue is highly correlated with quantity_invoices and 4 other fieldsHigh correlation
quantity_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
quantity_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
quantity_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with quantity_returns and 1 other fieldsHigh correlation
quantity_returns is highly correlated with gross_revenue and 5 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with avg_basket_sizeHigh correlation
avg_ticket is highly skewed (γ1 = 25.1569664) Skewed
frequency is highly skewed (γ1 = 24.87687084) Skewed
quantity_returns is highly skewed (γ1 = 21.9754032) Skewed
df_index has unique values Unique
customer_id has unique values Unique
avg_ticket has unique values Unique
recency_days has 33 (1.1%) zeros Zeros
quantity_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2022-02-13 17:14:15.876976
Analysis finished2022-02-13 17:14:31.714503
Duration15.84 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2316.666442
Minimum0
Maximum5714
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:31.779120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.35
Q1928.5
median2119.5
Q33536.25
95-th percentile5034.3
Maximum5714
Range5714
Interquartile range (IQR)2607.75

Descriptive statistics

Standard deviation1554.722712
Coefficient of variation (CV)0.6711033938
Kurtosis-1.010637904
Mean2316.666442
Median Absolute Deviation (MAD)1270.5
Skewness0.3426249769
Sum6875866
Variance2417162.71
MonotonicityStrictly increasing
2022-02-13T14:14:31.864665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
26701
 
< 0.1%
26581
 
< 0.1%
45641
 
< 0.1%
26601
 
< 0.1%
6131
 
< 0.1%
26621
 
< 0.1%
6151
 
< 0.1%
48241
 
< 0.1%
6191
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57141
< 0.1%
56951
< 0.1%
56851
< 0.1%
56791
< 0.1%
56581
< 0.1%
56541
< 0.1%
56481
< 0.1%
56371
< 0.1%
56361
< 0.1%
56261
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.37702
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:31.955333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.144523
Coefficient of variation (CV)0.1125803587
Kurtosis-1.206178196
Mean15270.37702
Median Absolute Deviation (MAD)1489
Skewness0.03219371129
Sum45322479
Variance2955457.892
MonotonicityNot monotonic
2022-02-13T14:14:32.042420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
181641
 
< 0.1%
129331
 
< 0.1%
129351
 
< 0.1%
149841
 
< 0.1%
170331
 
< 0.1%
137041
 
< 0.1%
129391
 
< 0.1%
170371
 
< 0.1%
141251
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2962
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.485061
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:32.134562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.46528
Coefficient of variation (CV)3.762955818
Kurtosis397.3013221
Mean2693.485061
Median Absolute Deviation (MAD)670.84
Skewness17.63537227
Sum7994263.66
Variance102727656.5
MonotonicityNot monotonic
2022-02-13T14:14:32.220307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
379.652
 
0.1%
745.062
 
0.1%
533.332
 
0.1%
731.92
 
0.1%
734.942
 
0.1%
3312
 
0.1%
719.781
 
< 0.1%
13375.871
 
< 0.1%
447.641
 
< 0.1%
567.361
 
< 0.1%
Other values (2952)2952
99.5%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.30929919
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:32.314717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.76092244
Coefficient of variation (CV)1.209170733
Kurtosis2.776517247
Mean64.30929919
Median Absolute Deviation (MAD)26
Skewness1.798052889
Sum190870
Variance6046.761059
MonotonicityNot monotonic
2022-02-13T14:14:32.422540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2218
74.7%
ValueCountFrequency (%)
033
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

quantity_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.724393531
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:32.521201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.857759893
Coefficient of variation (CV)1.547370886
Kurtosis190.7862392
Mean5.724393531
Median Absolute Deviation (MAD)2
Skewness10.76555481
Sum16990
Variance78.45991032
MonotonicityNot monotonic
2022-02-13T14:14:32.614276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2784
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2784
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

quantity_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1670
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1582.104447
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:32.709691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.35
Q1296
median640
Q31399.5
95-th percentile4403.25
Maximum196844
Range196843
Interquartile range (IQR)1103.5

Descriptive statistics

Standard deviation5705.291445
Coefficient of variation (CV)3.60614083
Kurtosis516.7418024
Mean1582.104447
Median Absolute Deviation (MAD)421
Skewness18.73765362
Sum4695686
Variance32550350.48
MonotonicityNot monotonic
2022-02-13T14:14:32.802164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1509
 
0.3%
889
 
0.3%
2888
 
0.3%
848
 
0.3%
2608
 
0.3%
2728
 
0.3%
2468
 
0.3%
2007
 
0.2%
2197
 
0.2%
Other values (1660)2885
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

quantity_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7644879
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:32.898597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.9329358
Coefficient of variation (CV)2.198786803
Kurtosis354.7788412
Mean122.7644879
Median Absolute Deviation (MAD)44
Skewness15.7061352
Sum364365
Variance72863.78981
MonotonicityNot monotonic
2022-02-13T14:14:32.989042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2843
 
1.4%
2037
 
1.2%
3535
 
1.2%
2935
 
1.2%
1934
 
1.1%
1533
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2530
 
1.0%
Other values (458)2628
88.5%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
315
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
926
0.9%
1028
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.99425671
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:33.086636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.915887985
Q113.11811111
median17.95344712
Q324.98179365
95-th percentile90.052125
Maximum4453.43
Range4451.279412
Interquartile range (IQR)11.86368254

Descriptive statistics

Standard deviation119.5320656
Coefficient of variation (CV)3.622814318
Kurtosis812.9647397
Mean32.99425671
Median Absolute Deviation (MAD)5.979018644
Skewness25.1569664
Sum97926.95393
Variance14287.91471
MonotonicityNot monotonic
2022-02-13T14:14:33.175203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.492758621
 
< 0.1%
15.413636361
 
< 0.1%
18.150615381
 
< 0.1%
17.943444441
 
< 0.1%
43.21921
 
< 0.1%
33.535714291
 
< 0.1%
9.4182926831
 
< 0.1%
19.557670451
 
< 0.1%
132.07389831
 
< 0.1%
16.807222221
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.30213285
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:33.264983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.91730769
median48.26785714
Q385.33333333
95-th percentile200.65
Maximum366
Range365
Interquartile range (IQR)59.41602564

Descriptive statistics

Standard deviation63.50535844
Coefficient of variation (CV)0.9435861206
Kurtosis4.908048776
Mean67.30213285
Median Absolute Deviation (MAD)26.26785714
Skewness2.066084007
Sum199752.7303
Variance4032.93055
MonotonicityNot monotonic
2022-02-13T14:14:33.355740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
2117
 
0.6%
4617
 
0.6%
1117
 
0.6%
516
 
0.5%
Other values (1248)2776
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1138323742
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:33.450299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008893504781
Q10.01633986928
median0.02589835169
Q30.04947858264
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03313871336

Descriptive statistics

Standard deviation0.4082205551
Coefficient of variation (CV)3.586155151
Kurtosis989.0663249
Mean0.1138323742
Median Absolute Deviation (MAD)0.0121968864
Skewness24.87687084
Sum337.8544866
Variance0.1666440216
MonotonicityNot monotonic
2022-02-13T14:14:33.540001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1198
 
6.7%
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.5%
0.0833333333315
 
0.5%
0.0909090909115
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0357142857113
 
0.4%
0.0256410256413
 
0.4%
Other values (1215)2635
88.8%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1198
6.7%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

quantity_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.88847709
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:33.636461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.864784
Coefficient of variation (CV)8.107685048
Kurtosis596.2019916
Mean34.88847709
Median Absolute Deviation (MAD)1
Skewness21.9754032
Sum103549
Variance80012.48604
MonotonicityNot monotonic
2022-02-13T14:14:33.725066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (203)705
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1978
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.252886
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:33.815233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172.2916667
Q3281.5480769
95-th percentile599.58
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)178.3105769

Descriptive statistics

Standard deviation283.8931966
Coefficient of variation (CV)1.201649645
Kurtosis102.7816879
Mean236.252886
Median Absolute Deviation (MAD)83.04166667
Skewness7.701877717
Sum701198.5657
Variance80595.34706
MonotonicityNot monotonic
2022-02-13T14:14:33.902460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
829
 
0.3%
869
 
0.3%
739
 
0.3%
608
 
0.3%
888
 
0.3%
758
 
0.3%
1368
 
0.3%
1057
 
0.2%
Other values (1968)2881
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.48997702
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-13T14:14:33.995032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.666666667
median13.6
Q322.14464286
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.47797619

Descriptive statistics

Standard deviation15.46012684
Coefficient of variation (CV)0.8839420902
Kurtosis29.32468467
Mean17.48997702
Median Absolute Deviation (MAD)6.6
Skewness3.436467798
Sum51910.25179
Variance239.015522
MonotonicityNot monotonic
2022-02-13T14:14:34.088708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1342
 
1.4%
941
 
1.4%
1639
 
1.3%
839
 
1.3%
1438
 
1.3%
1738
 
1.3%
536
 
1.2%
1136
 
1.2%
736
 
1.2%
1535
 
1.2%
Other values (896)2588
87.2%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
2591
< 0.1%
1771
< 0.1%
1481
< 0.1%
1271
< 0.1%
1051
< 0.1%
1041
< 0.1%
1011
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%

Interactions

2022-02-13T14:14:30.415351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.003545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.038100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.066171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.095245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.145252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.148504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.227973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.302255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.294692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.339048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.380812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.381672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.491258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.088642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.114575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.143490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.174547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.221877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.228527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.310790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.375806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.372926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.416936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.456196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.461101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.568338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.164428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.192650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.220456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.254515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.297069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.309653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.393543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.451396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.451893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.496743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.531098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.538902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.644843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.240336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.270101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.298198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.334412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.373845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.392173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.474664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.525698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.530579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.575179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.606590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.615924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.724966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.320017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.351144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.381516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.417060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.452833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.476693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.558710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.604326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.611770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.657673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.684376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.697793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.799673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.396291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.426435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.457377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.492976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.526274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.555875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.639012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.676709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.689241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.733732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.755859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.772828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.882852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.481122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.511244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.540036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.579800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.607381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.643992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.726221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.759045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.776043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.818572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.838135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.857440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.966841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.566428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.595132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.627560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.666103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.690231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.732775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.814326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.840721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.861778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.905249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.921070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.942697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:31.040800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.646222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.670444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.703715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.743079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.762267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.812098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.892023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.912281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.938052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.980830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.994789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.017560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:31.120810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.729334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.752439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.783793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.826838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.841891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.896177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.977752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.991287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.020280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.064130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.074800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.099517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:31.201766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.811096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.833115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.865060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.909873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.923210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.981854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.063123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.070872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.103308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.146414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.154526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.182945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:31.276102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.884299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.908710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:20.939189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.986019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.996202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.062359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.140586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.143533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.179333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.221495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.227091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.257690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:31.354251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:18.961556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:19.988442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:21.017552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:22.066189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:23.073540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:24.145842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:25.222213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:26.219776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:27.259758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:28.301992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:29.304577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T14:14:30.336863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-13T14:14:34.172077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-13T14:14:34.295272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-13T14:14:34.413359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-13T14:14:34.529573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-13T14:14:31.482743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-13T14:14:31.646880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysquantity_invoicesquantity_itemsquantity_productsavg_ticketavg_recency_daysfrequencyquantity_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.15222235.50000017.00000040.050.9705880.617647
11130473232.5956.09.01390.0171.018.90403527.2500000.02830235.0154.44444411.666667
22125836705.382.015.05028.0232.028.90250023.1875000.04032350.0335.2000007.600000
3313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000004.800000
4415100876.00333.03.080.03.0292.0000008.6000000.07317122.026.6666670.333333
55152914623.3025.014.02102.0102.045.32647123.2000000.04011529.0150.1428574.357143
66146885630.877.021.03621.0327.017.21978618.3000000.057221399.0172.4285717.047619
77178095411.9116.012.02057.061.088.71983635.7000000.03352041.0171.4166673.833333
881531160767.900.091.038194.02379.025.5434644.1444440.243316474.0419.7142866.230769
99160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysquantity_invoicesquantity_itemsquantity_productsavg_ticketavg_recency_daysfrequencyquantity_returnsavg_basket_sizeavg_unique_basket_size
29585626177271060.2515.01.0645.066.016.0643946.01.0000006.0645.00000066.000000
2959563617232421.522.02.0203.036.011.70888912.00.1538460.0101.50000015.000000
2960563717468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.500000
2961564813596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000066.500000
29625654148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.000000
2963565812479473.2011.01.0382.030.015.7733334.01.00000034.0382.00000030.000000
2964567914126706.137.03.0508.015.047.0753333.00.75000050.0169.3333334.666667
29655685135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333104.000000
2966569515060301.848.04.0262.0120.02.5153331.02.0000000.065.50000020.000000
2967571412558269.967.01.0196.011.024.5418186.01.000000196.0196.00000011.000000